Software Project Scheduling Problem Research Articles

The effect of autonomous team role selection in flexible projects

Today, most software projects are flexible, such as agile, extreme, or hybrid. In addition, this approach is increasingly being adopted for other types of projects. Most software project scheduling problems (SPSPs), more broadly, project scheduling problems (PSPs) and resource allocation methods, assume a fixed project structure. If we use these traditional methods, we cannot model and therefore understand why certain flexible, e.g., agile, policies are essential and how they help project management. This research shows how a matrix-based project planning approach can model team roles and synergies between employees. Extending the synergy-based software project scheduling problem, this study shows the effects of autonomous team role selection, highlighted in agile projects. The proposed model can manage (1) flexible projects, (2) synergies between employees, and (3) the difference between hard skills and soft skills. The proposed method is evaluated in a real-life case study. The proposed method is compared with existing, nonsynergy-based project scheduling methods. We found that positive synergy between employees can reduce the makespan of the project, and the best solution is given in autonomous team role selection.

Robust Scheduling of Multi-Skilled Workforce Allocation: Job Rotation Approach

This paper addresses scheduling challenges in software development organizations, specifically focusing on a novel version of the software project scheduling problem (SPSP). This enhanced model incorporates the dynamics of learning and forgetting phenomena, crucial in maintaining employee competencies, particularly when unexpected events such as absenteeism or shifts in project priorities occur. The paper introduces a new declarative reference model for SPSP, aimed at proactively managing the assignment of versatile programmers to tasks within an portfolio of IT projects, while considering the effects of forgetting. Implemented within a constraints programming environment, this model facilitates decision making in project management for software companies. It serves to find feasible solutions and identify conditions necessary to meet specified expectations. The effectiveness of the proposed SPSP model is demonstrated through numerical examples.

Multi-Objective Dynamic Software Project Scheduling: A Novel Approach to Handle Employee’s Addition

The software project scheduling (SPS) problem deals with optimal allocation of employees to tasks such that the project is completed within budget and schedule. This is well addressed in the absence of dynamic events and has been demonstrated in SPS research with the static scenarios only. The software development methodologies have evolved where customer requirements are not initially locked but are flexible to take into account ongoing additional requirements or changes. In this regard, ‘new employee addition’ has emerged as one of the critical dynamic events that could significantly disrupt the project budget and schedule. In this paper, the SPS is modelled as multi-objective optimization problem and a novel approach is presented to deal with ‘new employee addition’ dynamic event; that uses domain knowledge to generate a robust schedule. The proposed heuristic is further evaluated on 18 dynamic benchmark scenarios and 3 real-world instances. The results show that the proposed heuristic besides its capability to handle dynamic events also demonstrates good convergence while maintaining good distribution of solutions in a dynamic environment. The principal target audience for this research are project managers in software development organizations who will get optimal project reschedule under ‘new employee addition’ dynamic event particularly in complex, large scale and multi-project settings.

A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills Criterion

A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills Criterion

Multi-Skills Resource Constrained and Personality Traits Based Project Scheduling

One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimise project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.

Impacts of synergies on software project scheduling

The adequate allocation of human resources is one of the most important success factors in software projects. Although project teams can be regarded as complex systems in which a team’s performance is highly influenced by the interdependencies among team members, the allocation methods applied to date have focused only on individual skills and consider project teams as units of isolated workers. The existing software project scheduling problem (SPSP) is extended to (1) consider different skills and efficiencies of employees and (2) examine the pairwise synergies between them, as well as to (3) handle the flexible structure of the project that is used in flexible management, such as agile project management. To better understand the impact of synergies on the project’s cost, the solutions of the traditional and extended SPSP versions are analyzed and compared on the generated project networks. The results show not only that this factor has a highly significant impact but also that the project cost strongly depends on the structural parameters of the synergy network (e.g., topology, network size and degree centrality). Among these parameters, a low degree of centrality and some topologies, most notably star and circular networks, obtained the highest reduction in the projects’ total cost.

Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.

Solving Software Project Scheduling Problem using Whale Optimization Algorithm

In the software projects, one of the essential and difficult problems faced by the managers in the competitive software industry is the Software Project Scheduling Problem (SPSP). With the increase in employees and tasks’ numbers the problem is becoming an NP-hard problem. The goal of this proposal is to resolve the problem of the software project scheduling with Whale optimization algorithm (WOA) and utilized it on various instances from three datasets. In order to prove the soundness and viability of Whale optimization algorithm (WOA), we illustrated some experimental results. This algorithm gave good outcomes for datasets that have a few tasks but failed to find feasible solutions when increasing the number of tasks.

Software project scheduling problem in the context of search-based software engineering: A systematic review

This work provides a systematic literature review of the software project scheduling problem, in the context of search-based software engineering, and summarizes the main models, techniques, search algorithms and evaluation criteria applied to solve this problem. We also discuss trends and research opportunities. Our keyword search found 438 papers, published in the last 20 years. After considering the inclusion and exclusion criteria and performing the snowballing procedure, we have analyzed 37 primary studies. The results show the predominance of the use of evolutionary algorithms. The static model, in which the scheduling is performed once during the project, is considered in the majority of the papers. Synthetic instances are commonly used to validate the heuristic and hypervolume and execution time are the mostly applied evaluating criteria.

Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem

Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem

Survey Paper for Software Project Team, Staffing, Scheduling and Budgeting Problem

Software project scheduling is a standout amongst the most imperative scheduling zones looked by Software project management team. Software development companies are under substantial strain to finish projects on time, with budget, quality and with the suitable level of values and qualities. Inexperienced development team or potentially poor management can cause deferrals and costs that given scheduling and spending limitations are regularly unsuitable, prompting business basic disappointments. Software development companies frequently battle to convey extends on time, inside spending plan and with the required quality. For a fruitful project, both software building and software management are exceptionally vital. One conceivable reason for this issue is poor Software project management and, specifically, insufficient project scheduling and inadequate team staffing. Software project schedule issue is one of the essential and testing issues come across by the product project directors in the much focused software companies. Since matter is winding up hard with the expanding quantities of workers and tasks, just a couple of calculations exist and the execution is as yet not fulfilling, to build up an adaptable and powerful model for Software project arranging. In this paper we have attempted to expand a few systems and strategies and results yielded are explained.

Distribution in the placement of food in artificial bee colony based on changing factor

Nature inspired technique is moderately a new research paradigm that offers novel stochastic search techniques for solving many complex optimization problems. These techniques mimic the social and natural behavior of vertebrates. The basic idea behind modeling of such techniques is to achieve near optimum solutions to the large scale and complex optimization problems which can’t be solved using traditional or gradient based mathematical techniques. In this study a recently introduced nature inspired technique called Artificial Bee Colony, which is modeled on the intelligent foraging behavior of honey bees is selected as a framework. ABC has some inherent limitations like it favors exploration in comparison to exploitation. This causes loss in domain knowledge during the successive iterations. The proposed variant is embedded with levy probability distribution and abandon factor taken from cuckoo search, to balance the tradeoff between exploration and exploitation to obtain quality food sources (solutions) as well as improves the acceleration rate. The proposed variant is named as ABC with changing factor (CF-ABC). CF-ABC is based on an assumption that the potential food sources may have different probability distributions. CF-ABC is tested and compared with state-of-art algorithms over thirteen constrained benchmark optimization problems consulted from CEC 2006 and further validated on the Software Project Scheduling problem.

Retracted: An Improved Differential Evolution Solution for Software Project Scheduling Problem.

[This retracts the article DOI: 10.1155/2015/232193.].

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

Original scientific paper This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions.

New Approach for Solving Software Project Scheduling Problem Using Differential Evolution Algorithm

New Approach for Solving Software Project Scheduling Problem Using Differential Evolution Algorithm

An Improved Differential Evolution Solution for Software Project Scheduling Problem.

This paper proposes a differential evolution (DE) method for the software project scheduling problem (SPSP). The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. Thus the SPSP is a class of NP-hard problem, which requires a rigorous solution procedure which guarantees a reasonably better solution. Differential evolution is a direct search stochastic optimization technique that is fairly fast and reasonably robust. It is also capable of handling nondifferentiable, nonlinear, and multimodal objective functions like SPSP. This paper proposes a refined DE where a new mutation mechanism is introduced. The superiority of the proposed method is experimented and demonstrated by solving the SPSP on 50 random instances and the results are compared with some of the techniques in the literature.

A Max–Min Ant System algorithm to solve the Software Project Scheduling Problem

The Software Project Scheduling Problem is a specific Project Scheduling Problem present in many industrial and academic areas. This problem consists in making the appropriate worker-task assignment in a software project so the cost and duration of the project are minimized. We present the design of a Max–Min Ant System algorithm using the Hyper-Cube framework to solve it. This framework improves the performance of the algorithm. We illustrate experimental results and compare with other techniques demonstrating the feasibility and robustness of the approach, while reaching competitive solutions.

The software project scheduling problem: A scalability analysis of multi-objective metaheuristics

Computer aided techniques for scheduling software projects are a crucial step in the software development process within the highly competitive software industry. The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime, thus involving mainly both people-intensive activities and human resources. Two major conflicting goals arise when scheduling a software project: reducing both its cost and duration. A multi-objective approach is therefore the natural way of facing the SPS problem. As companies are getting involved in larger and larger software projects, there is an actual need of algorithms that are able to deal with the tremendous search spaces imposed. In this paper, we analyze the scalability of eight multi-objective algorithms when they are applied to the SPS problem using instances of increasing size. The algorithms are classical algorithms from the literature (NSGA-II, PAES, and SPEA2) and recent proposals (DEPT, MOCell, MOABC, MO-FA, and GDE3). From the experimentation conducted, the results suggest that PAES is the algorithm with the best scalability features.

A Hybrid Approach for Software Project Scheduling

This research work handles Software project scheduling problem with a hybrid approach. In this, a hybrid approach can handle combination of two different algorithms they are Event Based Scheduler(EBS) and Ant Colony Optimization(ACO). Which provide scheduling and also allocate the resources based on the project. The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at non-events. ACO is to assign the project tasks to suitable employees with required skills.

Solving software project scheduling problems with ant colony optimization

Software project scheduling problem (SPSP) is one of the important and challenging problems faced by the software project managers in the highly competitive software industry. As the problem is becoming an NP-hard problem with the increasing numbers of employees and tasks, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for SPSP, this paper proposes an ant colony optimization (ACO) approach which is called ACS-SPSP algorithm. Since a task in software projects involves several employees, in this paper, by splitting tasks and distributing dedications of employees to task nodes we get the construction graph for ACO. Six domain-based heuristics are designed to consider the factors of task efforts, allocated dedications of employees and task importance. Among these heuristic strategies, the heuristic of allocated dedications of employees to other tasks performs well. ACS-SPSP is compared with a genetic algorithm to solve the SPSP on 30 random instances. Experimental results show that the proposed algorithm is promising and can obtain higher hit rates with more accuracy compared to the previous genetic algorithm solution.